Automatic defect classification with invariant core classes

ABSTRACT

A method and apparatus is provided for automatically classifying a defect on the surface of a semiconductor wafer into one of, e.g., seven core classes: a missing pattern on the surface, an extra pattern on the surface, a deformed pattern on the surface, a particle on the surface, a particle embedded in the surface, a particle and a deformed pattern on the surface, or craters and microscratches on the surface. The defect may also be further classified into a subclass of arbitrarily defined defects defined by the user or preprogrammed in the apparatus. Embodiments include using a scanning electron microscope (SEM) capable of collecting electrons emitted from a plurality of angular sectors to obtain an image of the defect and a reference image containing topographical and location information, then analyzing this information to classify the defect. As the defects are classified, counts are maintained of the number of occurrences of each type of defect, and an alarm is raised if the defect count in a particular class exceeds a predetermined level. Thus, defects are accurately and reliably classified and monitored to enable early detection and cure of processing problems.

FIELD OF THE INVENTION

The present invention relates to a method and apparatus forautomatically classifying defects on the surface of an article. Theinvention has particular applicability for in-line inspection ofsemiconductor wafers during manufacture of high density semiconductordevices with submicron design features.

BACKGROUND ART

Current demands for high density and performance associated with ultralarge scale integration require submicron features, increased transistorand circuit speeds and improved reliability. Such demands requireformation of device features with high precision and uniformity, whichin turn necessitates careful process monitoring, including frequent anddetailed inspections of the devices while they are still in the form ofsemiconductor wafers.

Conventional in-process monitoring techniques employ an “inspection andreview” procedure wherein the surface of the wafer is initially scannedby a high-speed, relatively low-resolution inspection tool; for example,an opto-electric converter such as a CCD (charge-coupled device) or alaser. Statistical methods are then employed to produce a defect mapshowing suspected locations on the wafer having a high probability of adefect. If the number and/or density of the potential defects reaches apredetermined level, an alarm is sounded, indicating that a moredetailed look at the potential defect sites is warranted. This techniqueis known as “total density monitoring” of defects and produces astatistic called the “total defect density”.

When the defect density reaches a predetermined level, a review of theaffected wafers is warranted. The review process is carried out bychanging the optics of the inspection apparatus to a higher resolution,or using a different apparatus altogether. To perform the review, thedefect map is fed to the review apparatus and then redetection andreview of each suspected site is performed according to the defect map.

In the technique called redetection, the potential defect sites are eachcompared to a reference site, such as a comparable location on anadjacent, non-defective die on the same wafer, to positively determinethe presence of a defect. A more detailed review procedure is thereaftercarried out on the individual defect sites, such as scanning with a CCDto produce a relatively high-resolution image, which is then analyzedusing pattern recognition techniques to determine the nature of thedefect (e.g., a defective pattern, a particle, or a scratch).

Thus, detailed review procedures which classify defects and point tospecific corrective action to prevent future defects are typicallycarried out only after a large number of such defects are likely to haveoccurred. As a result, such defects remain largely undetected until aconsiderable number of wafers have been fabricated and have begun toexhibit problems caused by the defects. This late discovery of defectscan result in a low manufacturing yield and reduced productionthroughput.

Furthermore, because the defects are not classified until an alarm israised, and the alarm indicates only that a certain number of defectshas probably occurred, alarms may also be generated when only anacceptably small amount of defects of a serious type have occurred;i.e., there is no way to determine before the alarm is raised whetherthe potential defects are likely to warrant corrective action.

Moreover, optical devices such as CCDs are limited in their ability toanalyze and accurately identify defect types. Firstly, the resolution oftheir images is limited by the pixel size. Secondly, since they produceonly two-dimensional images, they cannot gather a large amount ofinformation regarding the topography of a defect, or whether it lies onthe surface or below the surface of the wafer. Thirdly, brightness dueto reflection of light from certain types of defects, such as scratches,overwhelms the CCD and may produce false defect counts and false alarms.Thus, the review is generally done manually, with an operator reviewingeach suspected site of interest.

Since it has recently been recognized that monitoring classified defectdensity is preferable to monitoring total defect density, variousmethods for classification of defects have been introduced. However, theefficiency of these methods is reduced because there is no agreed-uponset of defect classes. Specifically, different semiconductor fabricatorsconsider different defects to be important and, therefore, use differentsets of defect classes. Consequently, prior art classification methodsare tailored to specific users.

Another problem with prior art defect classification systems is that,because they are tailored to user-specific classes, they require manyexamples of defect images to be obtained for each defect class prior tobecoming operational. Consequently, prior art systems cannot be usedduring start-up and ramp-up of a production line.

There exists a need to quickly and meaningfully review semiconductorwafers and automatically classify the defects in order to identifyprocesses causing defects, thereby enabling early corrective action tobe taken. This need is becoming more critical as the density of surfacefeatures, die sizes, and number of layers in devices increase, requiringthe number of defects to be drastically reduced to attain an acceptablemanufacturing yield.

There also exists a need for a standardized set of classes whichcorrelate to the causes of defects. However, since different processlines may be sensitive to different defects from one to another, thereexists a further need for a defect classification system with theflexibility to accommodate the needs of various users.

There exists a further need for an automatic defect classificationsystem which is operable during start-up and ramp-up of a productionline and which requires no example defect images to become operable.

SUMMARY OF THE INVENTION

An object of the present invention is to provide a method and apparatusfor automatic, fast and reliable classification of defects insemiconductor wafers.

According to the present invention, the foregoing and other objects areachieved in part by a method of automatically classifying a defect onthe surface of an article, which method comprises imaging the surfaceand classifying the defect as being in one of a predetermined number ofinvariant core classes of defects. The defect may then be classified asbeing in one of an arbitrary number of variant subclasses of at leastone of the invariant core classes, at the option of a user of thepresent invention.

Another aspect of the present invention is a method of inspecting adefect on the surface of an article, which method comprises acquiring animage of the defect; obtaining a reference image; comparing the defectimage and the reference image to produce an estimated defect footprint;obtaining a magnified defect image; obtaining a magnified referenceimage; and comparing the estimated defect footprint, the magnifieddefect image and the magnified reference image to produce a defectfootprint.

A still further aspect of the present invention is an apparatus forcarrying out the steps of the above methods.

A still further aspect of the present invention is a computer-readablemedium bearing instructions for automatically classifying a defect onthe surface of an article, the instructions, when executed, beingarranged to cause one or more processors to perform the steps of theabove methods.

Additional objects and advantages of the present invention will becomereadily apparent to those skilled in this art from the followingdetailed description, wherein only the preferred embodiment of theinvention is shown and described, simply by way of illustration of thebest mode contemplated for carrying out the invention. As will berealized, the invention is capable of other and different embodiments,and its several details are capable of modifications in various obviousrespects, all without departing from the invention. Accordingly, thedrawings and description are to be regarded as illustrative in nature,and not as restrictive.

BRIEF DESCRIPTION OF THE DRAWINGS

Reference is made to the attached drawings, wherein elements having thesame reference numeral designations represent like elements throughout,and wherein:

FIG. 1 is a conceptual flow chart of defect classification according tothe present invention.

FIG. 2 graphically illustrates defect count by defect class as carriedout by the present invention.

FIG. 3 illustrates a semiconductor wafer to be inspected using thepresent invention.

FIGS. 4A–4C are representations of images of a defect to be inspected bythe present invention.

FIG. 5 is a flow chart illustrating sequential steps of a first phase ofa method according to the present invention.

FIG. 6 is a representation of a defect footprint to be analyzed by usingthe present invention.

FIG. 7 is a representation of a reference image corresponding to thedefect of FIG. 6.

FIGS. 8 a–13 are representations of defects to be analyzed using thepresent invention.

FIGS. 14 a and 14 b are a flow chart illustrating sequential steps of asecond phase of a method according to the present invention.

FIGS. 15 a and 15 b are representations of defects to be analyzed usingthe present invention.

FIG. 16 is a flow chart illustrating sequential steps of a third phaseof a method according to the present invention.

FIG. 17 is a block diagram that illustrates an embodiment of theinvention.

FIG. 18 is a schematic view of an SEM review station used to implementthe present invention.

FIGS. 19( a)–19(c) show how a defect might be viewed using sensors ofthe apparatus of FIG. 18.

FIG. 20( a) depicts a microscratch on the surface of a wafer to beinspected.

FIG. 20( b) is a cross-sectional view of the wafer of FIG. 20( a) takenalong line B—B.

FIGS. 20( c)–20(e) show how the microscratch might be viewed using thesensors of the apparatus of FIG. 18.

DESCRIPTION OF THE INVENTION

Conventional semiconductor wafer inspection techniques do not provideearly detection of serious defects, but rather only indicate that acertain amount of defects of all types have occurred. Furthermore,conventional inspection techniques are not capable of analyzing defectsin sufficient detail to provide information which leads to earlypositive identification of the defect source. The present inventionaddresses and solves these problems by providing automaticclassification of defects into meaningful categories, enabling readyidentification of processes causing defects, and enabling earlycorrective action to be taken.

According to certain embodiments of the methodology of the presentinvention, after a defect map of a semiconductor wafer has beengenerated, each defect site and a corresponding known non-defectivereference site is imaged by a scanning electron microscope (SEM) togather and store location and topographical data. This data is thenanalyzed to classify the defect as being in one of a number (e.g.,seven) of invariant core classes of defect, and further classified asbeing in one of an arbitrary number of sub-classes defined by the userof the invention.

FIG. 1 is a conceptual flow chart of automatic defect classificationinto core classes performed by the methodology of the present invention.A defect 1 is classified broadly as a pattern defect 2A or a particledefect 2B, and further placed into one of seven exemplary invariant coreclasses of defects: craters and microscratches on the wafer surface 3A,a missing pattern on the surface 3B, an extra pattern on the surface 3C,a deformed pattern on the surface 3D, a particle on the surface 3E, aparticle embedded in the surface 3F, or a particle and a deformedpattern on the surface 3G. Arbitrary sub-classes may include bridging(i.e., short circuiting) between neighboring wiring patterns, a smallparticle, a large particle, a broken line, a narrow pattern, etc. Thedefect classification of the present invention facilitates tracing thecauses of defects to their source, such as to a particular process stepor even to a particular piece of processing equipment.

A typical wafer processing sequence comprises the steps of deposition ofa material such as oxide, metal, or nitride, application of photoresist,development of the photoresist, etching and/or polishing, cleaning and,finally, inspection and review. While any parameter of theabove-mentioned process steps can introduce defects, most defects arecaused by foreign material. The classification of a defect as a particledefect 2B implies that foreign matter is still on the wafer surface.Therefore, if the defect is further classified as an embedded particledefect 3F, this implies that the defect occurred before or during thedeposition process, thus pointing out the appropriate corrective action.However, if the defect is classified as a particle on the surface 3E,further analysis of the foreign matter may be carried out, such as byspectroscopy, to identify the material composition of the particle, totrace its origin and thus pinpoint the cause of the defect.

On the other hand, the classification of a defect as a particular typeof pattern defect 2A (i.e., crater 3A, missing pattern 3B, extra pattern3C, or deformed pattern 3D) implies that the foreign material is nolonger present on the wafer, and only its effect is visible. Based onthe user's knowledge of their fabrication process, the user canconclude, for example, that craters and microscratches 3A were caused bya polishing process, a missing or extra pattern defect 3B, 3C, occurreddue to foreign material on top of the photoresist, or a deformed pattern3D was due to a photolithography problem such as a particle between thewafer and its supporting chuck which caused curvature and loss of focus.

As the defects are classified, counts are maintained of the number ofoccurrences of each type of defect so that an alarm may be raised if thedefect count in a particular class exceeds a predetermined level. Thus,defects are accurately and reliably classified and monitored to enableearly detection and cure of processing problems. Based on this type ofinformation, the user of the present invention can set tighterthresholds for defect counts. Additionally, the user can set differentalarm thresholds for different defect types depending on their inherentvariability (e.g., a particular defect type's tendency to increase whena serious process problem is occurring) or a particular defect'stendency to cause device failure (i.e., its “kill ratio”).

The utility of this classified defect density approach is illustrated inFIG. 2, which graphically depicts defect count by defect class A–G for anumber of wafers W1–W8. While it can be seen from FIG. 2 that the totalnumber of defects is approximately constant, the occurrence of defecttype D is dramatically increasing, though the occurrence of all otherdefect types is approximately constant. Thus, the user can set the alarmthreshold for defect type D lower, if defect D tends to cause devicefailure, or the user can set the alarm threshold at about 40 defects forall the defect types A–G, in order to detect an increase in any defecttype.

An embodiment of the present invention is illustrated in FIGS. 3–14 b.As shown in FIG. 3, a semiconductor wafer W to be inspected for defectshas a plurality of patterned integrated circuit dies 1000. Initially, adefect map is produced by conventional techniques, such as by scanningthe surface of a wafer with a high-speed inspection tool (a CCD, a laseror an SEM may be employed for this purpose), then using statisticalmethods, typically involving algorithms and/or grey-scale analysis, toidentify suspected locations on the wafer having a high probability ofhaving a defect.

Next, as shown in FIGS. 4A–4C, a redetection procedure is carried out ateach suspected defect location to determine the exact location of thedefect. A conventional CCD scanner or an SEM may be used to image apattern 10 at a suspected defect location, which is then compared to areference pattern 20 at a corresponding location on an adjacent or otherdie on the same wafer which is not suspected of having a defect. If adifference 30 is found between the suspected defective pattern 10 andthe reference pattern 20, the suspected defective pattern 10 isdetermined to be a defect, and the inventive analysis and classificationcommences.

FIG. 5 is a flow chart of the first phase of the inventive methodology,which produces a “defect footprint” or detailed image of the defectwhich is used in all subsequent analysis and classification of thedefect. In step 100, a picture 110 of the pattern previously determinedto be a defect (i.e., the defective pattern 10 from the redetectionprocedure) and its surrounding area on the wafer is acquired and stored.All images referred to in the present disclosure and claims arepreferably electronically stored (such as on DRAM, magnetic or opticalrecording media), and all disclosed image manipulation and analysis ispreferably automatically performed electronically. Acquired defectpicture 110 is preferably produced by an SEM capable of collectingelectrons, emitted from a wafer bombarded with electrons, from differentangular sectors and generating images of the defect and its surroundingarea from multiple perspectives. This type of SEM enables highresolution imaging and measurement of both topographic features andmaterial features of the imaged area. Such an SEM is described in U.S.Pat. No. 5,644,132 to Litman et al. and U.S. Pat. No. 4,941,980 toHalavee et al., the entire disclosures of which are hereby incorporatedherein by reference.

A picture 210 of a reference pattern corresponding to the location ofthe defect pattern is acquired at step 200, at the same magnification.Reference picture 210 can be a common picture for a plurality ofdefects, or can be a corresponding one for each defect, or can be takenfrom a computer aided design (CAD) drawing of the die. Reference picture210 is commonly the reference pattern 20 from the redetection procedure.

The acquired defect picture 110 and the acquired reference picture 210are compared at step 300 and an estimated defect footprint 410 isproduced at step 400. The estimated defect footprint 410 is a contourboundary of the defect; that is, a boundary curve drawn around thedefect which includes only the defect. Estimated defect footprint 410may not be a high-quality picture; i.e., it may contain noise.Therefore, an additional intermediate step is performed, wherein aportion of the acquired defect picture 110 containing the defect (i.e.the portion of acquired defect picture 110 different than the acquiredreference picture 210) is magnified at step 500 to produce zoomedacquired defect picture 510. Acquired reference picture 210 is alsomagnified at step 600, at an area corresponding to the magnified area ofacquired reference picture 110. The magnification at step 600 ispreferably carried out using an algorithm executed by computer-readablemedia to reduce the amount of memory required for this step, to producezoomed reference picture 610.

At step 700, estimated defect footprint 410, zoomed acquired defectpicture 510 and zoomed reference picture 610 are compared and refined toproduce defect footprint 810 at step 800. An example of a defectfootprint 810 is shown in FIG. 6, and an example of a correspondingzoomed reference picture 610 is illustrated in FIG. 7.

FIGS. 8 a–13, depicting a defect and its immediate surroundings,illustrate a second phase of the inventive methodology, which comprisesperforming a boundary analysis of the defect footprint and referenceimage to classify the defect in one of seven core classes. FIGS. 14 aand 14 b are a flow chart of the inventive second phase. The followingprocedures are performed automatically and are controlledalgorithmically, such as by a sequence of instructions on acomputer-readable medium.

Referring again to FIG. 7, and to FIG. 14 a, the zoomed referencepicture 610 is initially analyzed, in a step 1401 called referencesegmentation, to identify portions 610 a which correspond to a referencepattern and portions 610 b which correspond to a background to thereference pattern.

Next, referring to FIGS. 7, 8 a, and 14 a, common boundaries CB existingin both defect picture 800 and reference picture 610 are identified,defect boundaries DB which exist in the defect footprint 810 only areidentified, and reference boundaries RB which exist in the referencepicture 610 only (dotted line) are identified in step 1402. Thisinformation is analyzed in the following steps, along with referencesegmentation data and topographical data, to classify the defect intoone of the seven core classes.

Referring now to FIGS. 8 a, 8 b, 9, 10 and 14 a, in analyzing the defectfootprints 810–813, it is determined in step 1403 that defect boundaryDB in FIGS. 8 a, 8 b and 9, and DB1 in FIG. 10 has an open shape (i.e.,it is not a loop or polygonal), and that therefore the defect is apattern defect (step 1404 a).

Next, the reference segmentation data is consulted in step 1405, and thedefect shown in FIG. 9 is therefore classified in step 1406 a as amissing or deformed pattern defect (i.e., pattern data in the referenceimage is shown as background in the defect image). The defect associatedwith DB1 in FIG. 10 would also be classified as a missing patterndefect. It is then determined at step 1406 b whether another defectboundary (i.e., DB2 in FIG. 10) exists in the defect footprint. At thispoint, the defect in FIG. 9 is finally classified as a missing patterndefect in step 1406 c. However, if DB2 exists, such as depicted in FIG.10, the reference segmentation data is consulted again in step 1406 d,and the defect of DB2 is determined to be an extra pattern. Since DB1 isa missing pattern and DB2 is an extra pattern, the defect of FIG. 10 isfinally classified as a deformed pattern defect in step 1406 f. Incontrast, if DB1 and DB2 were both missing patterns, the defect would beclassified as a missing pattern defect at step 1406 e.

Referring now to FIG. 14 b, if the reference segmentation data showsthat the defect is an extra pattern defect at step 1405, as it would forthe defects in FIGS. 8 a and 8 b, defect footprints 810 and 811 arefurther analyzed for the existence of an additional defect boundary DBEin step 1407. If DBE does not exist, the defect (such as the defect ofFIG. 8 a) is classified in step 1408 a as an extra pattern defect. It isthen determined at step 1408 b whether another defect boundary such asDB2 in FIG. 10 exists in the defect footprint. If not, the defect inFIG. 8 a is finally classified as an extra pattern defect in step 1408c. However, if DB2 were to exist, the reference segmentation data wouldbe consulted again in step 1408 d, and the defect of DB2 would bedetermined to be an extra pattern or a missing pattern. If DB2 was anextra pattern, the defect would be classified as an extra pattern defectin step 1408 e, and if DB2 was a missing pattern, the defect would beclassified as a deformed pattern in step 1408 f.

If DBE exists (such as in the defect of FIG. 8 b), topographical datagathered by the SEM is consulted in step 1409 to check the flatness ofthe area proximal to DBE, and it is determined, if DBE is notsubstantially flat, that a particle is embedded under the defectiveextra pattern bounded by defect boundary DB. Consequently, the defect ofFIG. 8 b is classified in step 1410 as a particle and deformed patterndefect. On the other hand, if the area proximal to DBE is substantiallyflat, the defect would be classified as an extra pattern defect in step1411. It would then be determined if another defect boundary DB2 existsin the defect footprint, and the analysis of steps 1408 b–1408 f wouldbe carried out, as described above.

Referring to FIGS. 11 and 14 a, if defect boundary DB is determined tohave a closed shape in step 1403, as in defect footprint 814, it isconsidered to be a particle or isolated pattern defect in step 1404 b,and it is further determined, in step 1412, whether defect boundary DBintersects the common boundaries CB. If DB does not intersect CB, asshown in FIG. 11, the defect is an isolated defect. However, it could beeither an extra pattern or a particle on the surface of the wafer. Todetermine its classification, the topographical data is consulted instep 1413 to determine the flatness of the area bounded by DB. If thearea is substantially flat, the defect is classified as an extra patterndefect in step 1414. If the area is not substantially flat, it isclassified as a particle on the surface in step 1415.

FIGS. 12 a and 12 b show defect footprints 815, 816 wherein the defectboundary DB has a closed shape, but it would be determined at step 1412that DB intersects two of the common boundaries CB1 and CB2. If such adetermination is made, it is next determined, in step 1416, whether aboundary RB in reference image 610 which does not exist in defectfootprint 815 lies between the two common boundaries CB intersected bydefect boundary DB. If so, this defect is classified as a particle onthe surface in step 1417. However, if a third common boundary CB3 liesinside defect boundary DB, this defect is classified as an embeddedparticle at step 1418.

FIG. 13 shows a defect footprint 817 of the core class of craters andmicroscratches. A crater is a small gouge in the surface of the wafer. Amicroscratch is a very small scratch in the surface of the wafer.

The detection of craters and microscratches as depicted in FIG. 13 andthe particle defects depicted in FIGS. 12 a and 12 b is preferablyaccomplished using SEM multiple perspective imaging techniques, asdisclosed in the Halavee and Litman patents. These techniques will nowbe briefly discussed with reference to FIGS. 18–20. FIG. 18 shows an SEMreview station for determining depth information concerning defects inwafer structures using multiple SEM images. The SEM review station ofFIG. 18 helps determine whether a defect is a protrusion, like aparticle, or a recess, like a crater or microscratch.

The station shown in FIG. 18 comprises a plurality of sensors, alsocalled “detectors”. In this exemplary embodiment, there is a firstsensor 1890 located centrally with respect to an SEM column 1810. Firstsensor 1890 is also referred to as an “inside the column” detector.There is a second sensor 18100 located to the left, and a third sensor18110 located to the right, which are also referred to as “outside thecolumn” detectors. The station of station of FIG. 18 takes three imagesof wafer 1830 mounted on stage 1850 at substantially the same time bydirecting electron beam 1820 at wafer 1830 and detecting electrons 1880emitted from wafer 1830. The image produced by first sensor 1890 will bereferred to as a first image; that from second sensor 18100 as a secondimage; and that from third sensor 18110 as a third image. However, theselabels are for linguistic convenience only, and not meant to imply anyorder or sequence in image detection. Although the exemplary stationshown in FIG. 18 has three stationary sensors 1890, 18100 and 18110, itis possible to employ less than three movable sensors, and move them tothe three different positions of sensors 1890, 18100 and 18110 asrequired, since the images do not need to be taken simultaneously or inany particular order.

Due to the nature of SEM imaging, it will be appreciated that the firstimage has the perspective of electron beam 1820 (i.e., directlyoverhead) and appears as if the illumination is coming from first sensor1890 (i.e., also directly overhead). The second image has the sameidentical perspective as the first image (i.e., the perspective ofviewing from directly overhead), but appears as if the illumination iscoming from second sensor 18100 (i.e., illumination from the left). Thethird image, like the second and first images, has an identical overheadperspective, but appears as if the illumination is coming from the right(i.e., from third sensor 18110).

The three images thus each provide different information with respect tobright and dim features of the area of defect 1840, and all from anidentical perspective. Thus, a particular feature which appears flatwhen viewed from only directly overhead might look differently whenviewed in connection with the other two images. It should be noted thatthe defects are extremely small, and therefore some defects may onlyprove detectable in one of the three images.

In essence, the first and third images provide greyscale shadowinformation useful for characterizing the defect, and the second imageprovides an overhead, substantially flat view.

One way to appreciate the advantage of this multiple perspective imagingtechnique is to consider a bump protruding from a planar surface. Thisbump represents a defect. Viewing this bump from directly overhead, withillumination from overhead, the bump may appear as a flat pattern orstain as drawn in FIG. 19( a). Such a result might obtain from an imageproduced by first sensor 1890. Based on this image alone, it would bedifficult to characterize this defect as a flat circle, a protrudingbump, or a pit.

In an image produced from second sensor 18100, the perspective of theviewer is still directly overhead, but with the illumination appearingto come from the left. Under these conditions, the bump may appear ashaving a brighter part on the left, and a dimmer part on the right, asdrawn in FIG. 19( b). Thus, it may be determined that defect 1840 is aprotrusion and not a pit.

In an image produced from third sensor 18110, the perspective of theviewer is still directly overhead, but with the illumination appearingto come from the right. Under these conditions, the bump may appear ashaving a brighter part on the right, and a dimmer part on the left, asdrawn in FIG. 19( c). The determination of defect 1840 as a protrusionis thus confirmed. For example, to increase the level of confidence thegreyscales produced from the second sensor can be compared to thoseproduced by the third sensor.

On the other hand, assume defect 1840 is a crater. The image produced byfirst sensor 1890 might still be as drawn in FIG. 19( a). The imageproduced by second sensor 18100 would show a darker area on the left andlighter area on the right of the pit, as shown in FIG. 19( c). Likewise,the image produced by the output of third sensor 18110 would show adarker area on the right and a lighter area on the left of the pit, asdrawn in Fib. 19(b).

An example of the application of multiple perspective imaging toclassify defects according to the present invention will now bediscussed using FIGS. 20( a–e). FIG. 20( a) shows a part of waferstructure 1830 with defect 1840 as a microscratch. FIG. 20( b) shows asimplified cross-sectional view of wafer structure 1830 along referenceline B—B. As shown in FIG. 20( b), the microscratch (i.e., defect 1840)is a vertical scratch having a substantially wall-like left side and agently sloping right side. Although FIG. 20( a) shows upper and lowerends of this defect 1840, these are simply for reference and ease ofillustration. It is much more likely that the microscratch has gentlysloped ends.

FIG. 20( c) shows how this defect 1840 might appear from first sensor1890. Inasmuch as the illumination in the image provided from the dataof first sensor 1890 appears to be from overhead, no shadows appear; theimage from first sensor 1890 appears to be flat, and the microscratchappears to be only a linear feature. No depth information is availablein this first image.

FIG. 20( d) shows how this defect 1840 might appear from second sensor18100. The illumination appears to come from the left, and thus a shadowis caused by the substantially wall-like left side of the microscratch.Given the length of the shadow and the position of second sensor 18100,information as to the depth of the microscratch can be determined.

FIG. 20( e) shows how this defect 1840 might appear from third sensor18110. The illumination appears to come from the right in such an image,but the gently sloping right side of the microscratch gives no shadow.Because of the inclination of the wall-like left side of themicroscratch, the image provided from the output of third sensor 18110appears flat, and defect 1840 appears to be only a linear feature.

In this example, the defect 1840 was substantially linear. Defects willrarely have so simple a structure, and so the information available fromthe three images taken together will normally reveal enough to detectand to characterize most defects.

To summarize, the multiple perspective imaging technique provides depthinformation to classify defects as craters and microscratches orparticle defects using a plurality of images of a defect, with theimages being simultaneously taken with different SEM sensors atdifferent positions with respect to the defect. The plurality of imagesare compared. The differences in shading of the defect in the pluralityof images are analyzed to determine the depth information. Morespecifically, the analysis determines whether the defect is flat, is aprotrusion such as a particle defect as depicted in FIGS. 12 a and 12 b,or is a recess such as a crater or microscratch depicted in FIG. 13.

In another embodiment of the present invention, a monitor or set ofmonitors is provided, as shown in FIG. 18 as reference numerals M1–M3,to provide the user a display of each of the images produced by sensors1890, 18100 and 18110. Visual access to the three different images isadvantageous because the image from first sensor 1890 provides differentinformation than the other sensors 18100, 18110.

First sensor 1890, located in the center of the station, containsinformation regarding the composition of matter of the defect and itssurrounding area; i.e., it gives a visual indication of the presence ofone or a plurality of different materials by the contrast of shadingbetween different materials. For example, if two or more materials arepresent, this will be visually detectable because each material will beshaded differently. Second and third sensors 18100, 18110 produce imagesrelated to the topography of the defect, as discussed above, enablingthe identification of a defect as a bump or a hole in the wafer surface.By displaying the images from all three sensors 1890, 18100, 18110, theuser can see different aspects of the defect, thus enabling the user todetermine that a defect is, for example, a bump on the wafer surface andthat the bump is made of a different material than the surface.

The variant subclasses of defects carved out of the core classes arepreferably provided as “on/off modules” or “building blocks”configurable by the user, so that as the user develops their process anddetermines which types of defects need to be identified and monitored,subclasses can be added or deleted.

For example, FIG. 15 a illustrates a type of defect known as “bridging”,which can be detected by the inventive methodology as required by theuser as a subclass of the extra pattern core class of defect (e.g.,after step 1408, 1410 or 1411 in FIG. 14 b). Bridging, wherein twodiscrete patterns F1, F2 on the wafer surface are joined by an extrapattern D, almost certainly will cause short circuit failure of thecompleted device. Therefore, it is advantageous to be able to detect andclassify this type of defect. Boundary analysis of defect footprint 818determines that defect boundaries DB intersect at least one commonboundary CB corresponding to each of the two discrete features F1, F2 inthe reference image to classify the defect as bridging.

FIG. 15 b depicts another optional subclass of the missing pattern coreclass known as a “broken line”, which can be detected, e.g., after step1406 e in FIG. 14 a detects a missing pattern defect, by analyzing theimage of the area between DB 1 and DB2, for example by using techniquesdescribed in Litman et al. and Halavee et al. In other words, thefeature with the missing pattern is measured to further determine towhat extent the pattern is missing. A further example of the advantagesof this capability is another subclass of the missing pattern core classcalled a “narrow pattern”. Since a narrow pattern, such as depicted inFIG. 9, may cause device failure or inhibit device performance byincreasing electrical resistance, a user may wish to determine if apattern identified as a missing pattern defect is narrower than aprescribed width. By measuring the features in the areas around DB(e.g., defect footprint 812), the user can determine the width of theremaining pattern, and classify the defect as a narrow pattern defect ifthe width falls below a prescribed value.

Further subclassification of core classes of defects (i.e., subclassmodules) can result from measuring the distance from one pattern toanother to identify potential short circuits, such as measuring thedistance from an extra pattern defect as shown in FIG. 8 a, 8 b or 10 toan adjacent pattern, then classifying the defect in a separate subclassif the distance is less than a prescribed value. Still further, particledefects as shown in FIGS. 11, 12 a, and 12 b can be measured andsubclassified as “small particles” or “large particles” or particlesabove or below a prescribed area as desired by the user.

In another embodiment of the invention, the wafer surface can beoptically imaged in order to obtain information not available from SEMimages, such as the color of a layer under inspection, or the presenceof a particle embedded in a layer of glass (e.g. silicon dioxide) whichdoes not cause a bump on the surface of the glass large enough to bedetected by an SEM. Thus, additional subclass modules may be added asrequired by the user to more thoroughly inspect the wafer.

In a third phase of the inventive methodology, as the possible defectsindicated by the defect map are redetected, imaged and classified intocore classes and subclasses of defects, a count is maintained of thetotal number of defects in each class. When the total number of defectsin a specific one of the core classes or sub-classes is about equal toor exceeds a predetermined minimum acceptable number of that particulartype of defect, an alarm signal may be generated to alert the user. Inthis way, “class density monitoring” of defects is carried out, allowingearlier warning of faults in a particular process, and shorter responsetime for corrective actions.

FIG. 16 is a flow chart of the third phase of the inventive methodology.After a defect is classified into core class A at step 1601, asperformed according to the exemplary method depicted in FIGS. 14 a and14 b, the defect count for that core class is incremented at step 1602and then compared at step 1603 with a predetermined number x. If thedefect count is greater than or equal to x, an alarm signal is sent to,for example, a display at step 1604. If the defect count is less than x,no alarm signal is sent. Alternately, the alarm signal may be replacedby an alert signal used to alert a control processor that automaticallycontrols some aspect of a process to adjust the process to prevent thedefect in future processing.

FIG. 17 is a block diagram that illustrates an embodiment of theinvention. A computer system 1700 includes a bus 1702 or othercommunication mechanism for communicating information, and a processor1704 coupled with bus 1702 for processing information. Computer system1700 also includes a main memory 1706, such as a random access memory(RAM) or other dynamic storage device, coupled to bus 1702 for storinginformation and instructions to be executed by processor 1704. Mainmemory 1706 also may be used for storing temporary variables or otherintermediate information during execution of instructions to be executedby processor 1704. Computer system 1700 further includes a read onlymemory (ROM) 1708 or other static storage device coupled to bus 1702 forstoring static information and instructions for processor 1704. Astorage device 1710, such as a magnetic disk or optical disk, isprovided and coupled to bus 1702 for storing information andinstructions.

Computer system 1700 may be coupled via bus 1702 to a display 1712, suchas a cathode ray tube (CRT), for displaying information to a computeruser. An input device 1714, including alphanumeric and other keys, iscoupled to bus 1702 for communicating information and command selectionsto processor 1704. Another type of user input device is cursor control1716, such as a mouse, a trackball, or cursor direction keys forcommunicating direction information and command selections to processor1704 and for controlling cursor movement on display 1712.

An SEM 1718 inputs data representative of images of a semiconductorwafer under inspection, as discussed above, to bus 1702. Such data maybe stored in main memory 1706 and/or storage device 1710, and used byprocessor 1704 as it executes instructions. SEM 1718 may also receiveinstructions via bus 1702 from processor 1704.

The invention is related to the use of computer system 1700 forinspecting the surface of a semiconductor wafer for defects. Accordingto one embodiment of the invention, inspection of the surface of asemiconductor wafer, including classification of surface defects, isprovided by computer system 1700 in response to processor 1704 executingone or more sequences of one or more instructions contained in mainmemory 1706. Such instructions may be read into main memory 1706 fromanother computer-readable medium, such as storage device 1710. Executionof the sequences of instructions contained in main memory 1706 causesprocessor 1704 to perform the process steps described above. One or moreprocessors in a multi-processing arrangement may also be employed toexecute the sequences of instructions contained in main memory 1706. Inalternative embodiments, hard-wired circuitry may be used in place of orin combination with software instructions to implement the invention.Thus, embodiments of the invention are not limited to any specificcombination of hardware circuitry and software. The programming of theapparatus is readily accomplished by one of ordinary skill in the artprovided with the flow chart of FIGS. 14 a and 14 b.

The term “computer-readable medium” as used herein refers to any mediumthat participates in providing instructions to processor 1704 forexecution. Such a medium may take many forms, including but not limitedto, non-volatile media, volatile media, and transmission media.Non-volatile media include, for example, optical or magnetic disks, suchas storage device 1710. Volatile media include dynamic memory, such asmain memory 1706. Transmission media include coaxial cable, copper wireand fiber optics, including the wires that comprise bus 1702.Transmission media can also take the form of acoustic or light waves,such as those generated during radio frequency (RF) and infrared (IR)data communications. Common forms of computer-readable media include,for example, a floppy disk, a flexible disk, hard disk, magnetic tape,any other magnetic medium, a CD-ROM, DVD, any other optical medium,punch cards, paper tape, any other physical medium with patterns ofholes, a RAM, a PROM, and EPROM, a FLASH-EPROM, any other memory chip orcartridge, or any other medium from which a computer can read.

Various forms of computer-readable media may be involved in carrying outone or more sequences of one or more instructions to processor 104 forexecution. For example, the instructions may initially be borne on amagnetic disk of a remote computer. The remote computer can load theinstructions into its dynamic memory and send the instructions over atelephone line using a modem. A modem local to computer system 1700 canreceive the data on the telephone line and use an infrared transmitterto convert the data to an infrared signal. An infrared detector coupledto bus 1702 can receive the data carried in the infrared signal andplace the data on bus 1702. Bus 1702 carries the data to main memory1706, from which processor 1704 retrieves and executes the instructions.The instructions received by main memory 1706 may optionally be storedon storage device 1710 either before or after execution by processor1704.

The inventive semiconductor wafer inspection technique enables defectsto be separately and reliably classified as particle or pattern defects,and as on-surface or below-surface (embedded) defects. It also providesearly quantification and notification of these meaningfully classifieddefects, thereby facilitating investigation of the causes of thedefects, and enabling early corrective action to be implemented. Thus,the present invention contributes to the maintenance of productionthroughput. Moreover, the inventive methodology classifies defects byimaging the wafer surface and performing boundary analysis and/ortopographical measurement of its features, and so does not requireexamples of defect images for each class prior to being operational.Therefore, unlike prior art defect classification systems, the presentinvention can be used during the start-up and ramp-up of a productionline.

The present invention is applicable to the inspection of anysemiconductor wafer, and is especially useful for in-process inspectionof semiconductor wafers during manufacture of high density semiconductordevices with submicron design features.

The present invention can be practiced by employing conventionalmaterials, methodology and equipment. Accordingly, the details of suchmaterials, equipment and methodology are not set forth herein in detail.In the previous descriptions, numerous specific details are set forth,such as specific materials, structures, chemicals, processes, etc., inorder to provide a thorough understanding of the present invention.However, as one having ordinary skill in the art would recognize, thepresent invention can be practiced without resorting to the detailsspecifically set forth. In other instances, well known processingstructures have not been described in detail, in order not tounnecessarily obscure the present invention.

Only the preferred embodiment of the invention and but a few examples ofits versatility are shown and described in the present disclosure. It isto be understood that the invention is capable of use in various othercombinations and environments and is capable of changes or modificationswithin the scope of the inventive concept as expressed herein.

1. A method of automatically classifying defects on the surface of anarticle, which method comprises at least: imaging the surface;classifying each of the defects as being in one of a predeterminednumber of invariant core classes of defects; determining a total numberof defects in each of the core classes; and generating an alarm signalwhen the total number of defects in a specific one of the core classesis equal to or greater than a first predetermined number.
 2. The methodaccording to claim 1, wherein the core classes of defects comprise amissing pattern on the surface, an extra pattern on the surface, aparticle on the surface, a particle embedded in the surface, andmicroscratches on the surface.
 3. The method according to claim 1,comprising imaging the surface with a scanning electron microscope. 4.The method according to claim 1, comprising further classifying one ofthe defects as being in one of an arbitrary number of variant subclassesof at least one of the invariant core classes.
 5. The method accordingto claim 4, comprising classifying a plurality of defects on the surfaceof the article; and determining a total number of defects in each of thesubclasses.
 6. The method according to claim 5, comprising generating analarm signal when the total number of defects in a specific one of thesubclasses is about equal to or greater than a second predeterminednumber.
 7. A computer-readable medium bearing instructions forautomatically classifying defects on the surface of an article, saidinstructions, when executed, being arranged to cause one or moreprocessors to perform the steps of: imaging the surface; classifyingeach of the defects as being in one of a predetermined number ofinvariant core classes of defects; determining a total number of defectsin each of the core classes; and generating an alarm signal when thetotal number of defects in a specific one of the core classes is aboutequal to or greater than a first predetermined number.
 8. Thecomputer-readable medium according to claim 7, wherein the core classesof defects comprise a missing pattern on the surface, an extra patternon the surface, and a particle on the surface.
 9. The computer-readablemedium according to claim 7, wherein the instructions, when executed,are arranged to cause the one or more processors to perform the step ofimaging the surface with a scanning electron microscope.
 10. Thecomputer-readable medium according to claim 7, wherein the instructions,when executed, are arranged to cause the one or more processors toperform the step of classifying one of the defects as being in one of anarbitrary number of subclasses of at least one of the invariant coreclasses, the subclasses being of arbitrarily defined defects.
 11. Thecomputer-readable medium according to claim 10, wherein theinstructions, when executed, are arranged to cause the one or moreprocessors to perform the steps of: classifying a plurality of defectson the surface of the article; and determining a total number of defectsin each of the subclasses.
 12. The computer-readable medium according toclaim 11, wherein the instructions, when executed, are arranged to causethe one or more processors to perform the step of generating an alarmsignal when the total number of defects in a specific one of thesubclasses is about equal to or greater than a second predeterminednumber.
 13. The computer-readable medium according to claim 7, whereinthe instructions, when executed, are arranged to cause the one or moreprocessors to perform the step of imaging by acquiring a plurality ofimages using a plurality of spaced-apart detectors.
 14. Thecomputer-readable medium according to claim 13, wherein theinstructions, when executed, are arranged to cause the one or moreprocessors to acquire the images by causing the detectors to collectelectrons.
 15. An apparatus for classifying defects on the surface of anarticle, comprising: an imager to produce an image of the defect and areference image; a storage device to store the defect image and thereference image; a comparator to compare the defect image and thereference image; a processor to classify the defect as being in one of apredetermined number of invariant core classes of defects; a firstcounter for counting the number of defects in each of the core classes;and a first signal generator for generating an alarm signal when thetotal number of defects in a specific one of the core classes is aboutequal to or greater than a first predetermined number.
 16. The apparatusof claim 15, wherein the imager is a scanning electron microscope (SEM).17. The apparatus of claim 16, further comprising a plurality ofspaced-apart detectors and a monitor to display images produced by theplurality of detectors.
 18. The apparatus of claim 16, wherein the SEMcomprises an SEM column, wherein a first one of the plurality ofdetectors is disposed inside the SEM column and a second one of theplurality of detectors is disposed outside the SEM column.
 19. Theapparatus of claim 18, further comprising a first monitor for displayingan image produced by the first detector, and a second monitor fordisplaying an image produced by the second detector.
 20. The apparatusof claims 15, wherein the storage device is a digital storage device.21. The apparatus of claim 15, further comprising of processor forclassifying the defect as being in one of arbitrary number of subclassesof at least one of the invariant core classes, the subclasses being ofarbitrarily defined defects.
 22. The apparatus of claim 21, furthercomprising a second counter for counting the number of defects in eachof the subclasses and a second signal generator for generating an alarmsignal when the total number of defects in a specific one of thesubclasses is about equal to or greater than a second predeterminednumber.